CN106027559A - Network session statistical characteristic based large-scale network scanning detection method - Google Patents
Network session statistical characteristic based large-scale network scanning detection method Download PDFInfo
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
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Abstract
The invention provides a network session statistical characteristic based large-scale network scanning detection method, and belongs to the technical field of internet security. The network session statistical characteristic based large-scale network scanning detection method comprises the steps of screening and classifying captured original network data according to protocol types; then restoring each session in the data and clustering the sessions according to source IPs; counting the number of abnormal returned value of all sessions of each IP, calculating a ratio of the number of the abnormal returned values to the number of normal returned values; analyzing request modes of all sessions of each IP, observing whether the request modes corresponding to the abnormal returned values are accordant; judging whether an attack behavior exists based on the ratio and the request modes; and when the attack behavior exists, obtaining IP information of an attacker and an attacked target, and correspondingly performing processing measures. The network session statistical characteristic based large-scale network scanning detection method is very high in practical feasibility and universal, can identify the scanning condition of any IP made by the attacker, and has a chance to detect an unknown attacking way.
Description
Technical field
The invention belongs to internet security technical field, specifically refer to a kind of large scale network based on BlueDrama statistical nature and sweep
Retouch detection method.
Background technology
Along with the development of the Internet and popularizing of computer technology, global economy increases increasingly faster, and the life of people the most more comes
The most convenient, but the most also bring network security problem miscellaneous and hidden danger.The development of Internet technology makes network
The risk attacked is increasing with chance, and large-scale attack once occurs, and it causes consequence also will be the most serious.
How to carry out network security defense work to be increasingly valued by the people.The Perfected process of reply network attack is i.e. to set up one
The system of overall safety, but so require that all of user can authenticate oneself and must use various encryption method and access
Control measure protect data, this actual it appear that hardly possible thing.Based on this, network attack detection technology is for net
Network safety just seems particularly significant, as long as there is malicious act in network traffics, it is possible to detect to greatest extent and exactly
Arrive, then take corresponding treatment measures, just can the impact that this malicious act causes be dropped the lowest.
Hacker is when doing network attack, and scanning is often the first step.Complete once successful network attack, first seek to receive
The various information of collection target, then target can be analyzed by assailant according to these information, finds the leakage that goal systems exists
Hole, thus these leaks or authority just can be utilized to carry out next step action.If the scanning behavior of hacker can be detected, it is possible to
The patching bugs when attack does not causes substantive harm, prevents the most possible aggressive behavior.But along with big data age
Arriving, the corresponding network traffics produced also are sharply increasing, and how to differentiate abnormal flow, and accurately high in this mass data
Detect scanning attack behavior to effect, be a great problem in present network safety filed.
Currently, with respect to by flow analysis network when present in research some achievement of network malicious act.Have perhaps
Many documents propose the method for network attack detection from different perspectives.Existing document is retrieved, compares and analyzed, screening
Go out following several the technical information relevant to network attack detection:
List of references 1: Zhang Mengmeng disclosed in JIUYUE in 2011 28 days " for the fast matching method of Network Intrusion Detection System ",
Propose a kind of based on snort rule quick character string matching method, utilize network normal flow hardly with any virus number
The fact that match according to name, detect network intrusions behavior.
List of references 2: Wang Pinghui, Zheng Qinghua, Niu Guolin etc. are " based on traffic statistics feature disclosed in 21 days April in 2008
TCP detection algorithm " in, with the similarity between host number and the ratio of port number and accessed host port set as base
Plinth, uses nonparametric accumulation and cusum method to be analyzed flow statistical nature, it may be judged whether ports having scanning behavior.
At present a lot of document all labors mode of network attack, for these modes, simply proposes a series of strick precaution
Thinking, but many thinkings are not applied in reality, and availability is poor.It addition, the attack detection method that a lot of documents put forward
The aggressive behavior for certain triangular web or target can only be detected.In the face of large-scale network scan attack, solution is also
It not a lot.And existing a lot of network attack detection technology can only detect for the specific attack pattern of a certain kind, example
Scheme as proposed in list of references 2 can only detect the scanning for port, has certain limitation.
Summary of the invention
Present invention is primarily targeted at the detection method that a kind of large-scale scanning behavior based on BlueDrama statistical nature is provided,
Analyze the session in network, utilize the large scale network base line summed up in practice to be characterized, carry out attacking test problems
Decompose and simplify, it is judged that whether flow having attack data, and identifies aggressive behavior, improve accuracy of judgement as much as possible simultaneously
Rate, reduction rate of false alarm.
The detection method of the large-scale scanning behavior based on BlueDrama statistical nature that the present invention provides, comprises the steps:
Step 101: capture raw network data stream from node；
Step 102: data are carried out sifting sort by protocol type；
Step 103: reduce each session from data, it will words are polymerized to different classes according to not homology IP；
Step 104: add up the exception return value number M of all sessions of each IP, and calculate abnormal return value and normal return value
Number ratio K；M, K are positive number；
User can be for the self-defined request mode of different agreement and abnormal return value；
Step 105: analyze the request mode of all sessions of each IP, the request mode observing abnormal return value corresponding is the most consistent；
Step 106: judge in data whether aggressive behavior, if it has, perform step 107；If it did not, go to step 108 execution；
Step 107: obtain assailant and the IP information of target of attack, and measure of correspondingly handling it；
Step 108: detection terminates.
In described step 106, it may be judged whether have the aggressive behavior concrete grammar to be: setting threshold value A and threshold value B, A, B are just
Number, when abnormal return value number exceedes threshold value A, and when ratio K exceedes threshold value B, checks corresponding the asking of abnormal return value further
Modulus formula whether reach 90% consistent, if so, think and there is abnormal flow, have aggressive behavior；Otherwise it is assumed that do not attack
Behavior.
The method utilizing the present invention to provide carries out Network scan detection, has the following advantages and good effect:
(1) practical feasibility of the inventive method is the highest, can realize each step of detection to computer program working as
In, thus realizing Aulomatizeted Detect function, efficiency is much higher compared with manual detection, and can save resource.
(2) the inventive method does not detect, as long as have return value and ask modulus just for field specific in session
The protocol conversation of formula, all can use the inventive method to detect, have universality.
(3) not limiting the IP in flow in the inventive method, it can identify assailant and scan any IP
Situation, whereby it can be detected that large-scale network sweep behavior.
(4) detect not for certain specific known attack mode due to this method, thus have probability unknown the attacking of detection
Hit mode.
Accompanying drawing explanation
Fig. 1 is the large scale network scanning detection method schematic flow sheet based on BlueDrama statistical nature of the present invention.
Detailed description of the invention
Below in conjunction with accompanying drawing and example, the present invention is described in further detail.
The present invention has gone out, by analysis and summary, the behavior characteristics that hacker is scanned for different agreement, and focus is locked in flow
In return value and request mode on.For this 2 point, it is proposed that large-scale scanning detection side based on BlueDrama statistical nature
Method.By to the definition of abnormal return value and the comparison of request mode, it is judged that whether flow meets aggressive behavior feature, thus knows
Do not go out aggressive behavior that may be present.The characteristic simultaneously network sweep embodied on request time, adds testing mechanism and works as
In, improve the accuracy rate of analysis result.
First the original flow grabbed is classified by procotol, then propose to meet attacking of this agreement for different agreement
Hit feature, and flow is matched with attack signature, when there being flow to meet feature, then judge to there is aggressive behavior, then enter
One step analyzes this partial discharge, obtains the information of assailant and the person of being hacked.
As it is shown in figure 1, the large scale network scanning detection method based on BlueDrama statistical nature of the present invention include step 101～
Step 108, each step is described as follows:
Step 101: capture raw network data stream from node；
Step 102: data are carried out sifting sort by protocol type, such as, have HTTP (HTML (Hypertext Markup Language)), FTP (literary composition
Part host-host protocol), the agreement such as IMAP (Internet Mail Access Protocol), SNMP (Simple Network Management Protocol)；
These sessions are polymerized to different classes according to not homology IP by step 103: reduce each session from data；
Step 104: add up the exception return value number of all sessions of each IP, and calculate abnormal return value and normal return value
Number ratio K；User can be for the self-defined abnormal return value of different agreement and corresponding request mode.K is positive number.
Step 105: analyze the request mode of all sessions of each IP, the request mode observing abnormal return value corresponding is the most consistent；
Step 106: judge in data whether aggressive behavior.If it has, then go to step 107；If it is not, go to step 108；
Step 107: after analyzing, take the target of attack IP information etc. of the IP of assailant, assailant；To aggressive behavior, then may be used
Correspondingly handle it measure.
Step 108: this detection terminates.
As long as agreement all has corresponding return value to the different request results of same request pattern, this method can be used to carry out classification inspection
Survey.
In step 104～105, the present invention is directed to HTTP, FTP, snmp protocol defines respective request pattern and exception returns
Value, these several return values and the request mode of present invention definition are applied in large scale network scanography all have preferable effect.
(1) http protocol: the return value that http protocol is corresponding is the return value of each HTTP request, including 200,302,
304,401,403,404 etc., wherein 401,403,404 it is defined as abnormal return value；Corresponding request mode is each HTTP
The URL (URL) that request is corresponding.The even URL of the HTTP request that certain IP sends is essentially identical, return value
But major part is abnormal, then meet attack signature.
(2) File Transfer Protocol: the return value that File Transfer Protocol is corresponding is the return value of the return FTP order of submission every time, including 230,
220,210,150,331 etc., wherein 331 (needing login account), 530 (not logining) are defined as abnormal return value；Right
The request mode answered, for continually entering username and password, is attempted connecting ftp server.Even certain IP continuously attempts at short notice
Login different ftp server, but fail, then it is assumed that it meets attack signature.
(3) snmp protocol: every time SNMP request all can a corresponding oid, the request mode that snmp protocol is corresponding is often
Corresponding oid (object identifier of system) in secondary snmp request, as IBM be 1.3.6.1.4.1.2}, Cisco be 1.3.6.1.4.1.9},
These companies oneself definition has the oid of each system resource, has system, and name, tcp etc., such as 1.3.6.1.4.1.1.2.1.4 are just
Represent system user name；Corresponding exception return value is the corresponding return information of oid (value).Even certain IP is constantly to difference
Equipment sends the snmp request request of identical No. oid, and is same or with several oid, thus take a lot of system or
Facility information, then it is assumed that it meets attack signature.
For the definition to http, ftp, snmp these three agreement of the application above, through experiment, there is preferable Detection results.Its
The request mode of his agreement and abnormal return value, analysis personnel are referred to these three agreement self-defining.
Preferably, in step 106, the rate of false alarm that the present invention will use following mechanism to reduce detection method.First, it is contemplated that
Access website or server and abnormal situation occurs, only just think when abnormal return value large percentage and flow there may be
Abnormal flow；If threshold value A and threshold value B, A, B be positive number, and when abnormal return value number M exceedes threshold value A, and ratio K surpasses
When crossing threshold value B, it is believed that there may exist abnormal flow.Judge the request mode the most basic that abnormal return value is corresponding the most again
Cause, if reach 90% consistent, then it is determined that large-scale scanning, data have attack signature, request promoter to be attacker.
It addition, generally assailant uses auxiliary program to do large-scale scanning, the flow of this program scanning and normal discharge have one relatively
Big difference, the time interval of request is the shortest i.e. every time, it is possible to the request further analyzing doubtful flow the most all collects
In within the time of a certain preseting length, the most just think that this partial discharge is attack traffic.
Preferably, in step 106, the present invention has added up abnormal return value number, through analytic statistics, when abnormal return value occurs super
Cross 1000, and ratio more than 70% time, it is believed that be likely to be and doing large-scale scanning behavior, there is aggressive behavior.
Preferably, all above step all can be realized by program, as long as the program finished writing being deployed on certain flow node,
Just these flows can be carried out the detection of automatization.Using this Programmable detection compared with manual detection, it has process mass data
Ability, the efficiency of detection is the highest.
In the example of the present invention, capture all data traffics of this network interface from the network interface of a certain unit, found the HTTP of a certain IP
The return value of request has many 404 and 401, then according to the detecting step of the present invention pays close attention to the URL of these requests, finds this
A little URL are basically identical, be login.html, and the time interval every time asked is the least, and the network meeting present invention definition is swept
Retouching feature, it is judged that this partial discharge is network sweep flow, this IP is assailant IP.
Afterwards original flow is analyzed checking, finds that this IP strictly make use of the home router leak of certain specific model doing
Large-scale scanning.
Claims (4)
1. the detection method of a large-scale scanning behavior based on BlueDrama statistical nature, it is characterised in that realize step such as
Under:
Step 101: capture raw network data stream from node；
Step 102: data are carried out sifting sort by protocol type；
Step 103: reduce each session from data, it will words are polymerized to different classes according to not homology IP；
Step 104: add up the exception return value number of all sessions of each IP, and calculate abnormal return value and normal return value
Number ratio K, K is positive number；
Step 105: analyze the request mode of all sessions of each IP, the request mode observing abnormal return value corresponding is the most consistent；
Step 106: judge whether have aggressive behavior in data, if it has, perform step 107；Otherwise go to step 108 execution；
Determining whether that aggressive behavior concrete grammar is: set threshold value A and threshold value B, A, B are positive number, when abnormal return value
Number exceedes threshold value A, and when ratio K exceedes threshold value B, checks whether request mode corresponding to abnormal return value reaches 90% further
Consistent, if so, think and there is abnormal flow, have aggressive behavior；Otherwise it is assumed that there is no aggressive behavior；
Step 107: obtain assailant and the IP information of target of attack, and measure of correspondingly handling it；
Step 108: detection terminates.
The detection method of a kind of large-scale scanning behavior based on BlueDrama statistical nature the most according to claim 1, its
It is characterised by that described exception return value and request mode are defined as follows in HTTP, FTP and snmp protocol:
(1) http protocol: the return value that return value is each HTTP request that http protocol is corresponding, definition is abnormal to be returned
Value includes 401,403 and 404；Corresponding request mode is that the corresponding URL, URL of each HTTP request represents unified money
Source location accords with；
(2) File Transfer Protocol: the return value that File Transfer Protocol is corresponding is the return value of the return FTP order every time submitted to, defines different
Often return value includes 331 and 530；Corresponding request mode, for continually entering username and password, is attempted connecting ftp server；
(3) snmp protocol: SNMP request can corresponding an oid, oid be all the object identifier of system every time, and SNMP assists
The request mode of view is corresponding oid in each snmp request, and corresponding exception return value is the corresponding return information of oid；If certain
Individual IP constantly sends the snmp request request of identical No. oid to distinct device, and is same or with several oid, then it is assumed that
It meets attack signature.
The detection method of a kind of large-scale scanning behavior based on BlueDrama statistical nature the most according to claim 1, its
Being characterised by, arranging threshold value A in described step 106 is 1000, and threshold value B is 70%.
The detection method of a kind of large-scale scanning behavior based on BlueDrama statistical nature the most according to claim 1, its
Being characterised by, in described step 106, when abnormal return value number exceedes threshold value A, and ratio K exceedes threshold value B, and different
The request mode that often return value is corresponding reach 90% consistent time, it is judged that the request of this data flow the most all concentrates on a certain setting
In the time of length, the most then judge this data traffic as attack traffic, otherwise, it is determined that this data traffic is not attack traffic.
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| CN106850687A (en) * | 2017-03-29 | 2017-06-13 | 北京百度网讯科技有限公司 | Method and apparatus for detecting network attack |
| CN107454109A (en) * | 2017-09-22 | 2017-12-08 | 杭州安恒信息技术有限公司 | A kind of network based on HTTP flow analyses is stolen secret information behavioral value method |
| CN107733873A (en) * | 2017-09-19 | 2018-02-23 | 北京北信源软件股份有限公司 | A kind of viral early warning system and method |
| CN108471430A (en) * | 2018-07-03 | 2018-08-31 | 杭州安恒信息技术股份有限公司 | A kind of Internet of Things embedded-type security means of defence and device |
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| CN112953938A (en) * | 2021-02-20 | 2021-06-11 | 百度在线网络技术（北京）有限公司 | Network attack defense method and device, electronic equipment and readable storage medium |
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| CN108989294A (en) * | 2018-06-28 | 2018-12-11 | 杭州安恒信息技术股份有限公司 | A kind of method and system for the malicious user accurately identifying website visiting |
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| CN110166486A (en) * | 2019-06-14 | 2019-08-23 | 李啟锋 | Website protection method, device and computer readable storage medium |
| CN112953938A (en) * | 2021-02-20 | 2021-06-11 | 百度在线网络技术（北京）有限公司 | Network attack defense method and device, electronic equipment and readable storage medium |
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| CN113452707B (en) * | 2021-06-28 | 2022-07-22 | 华中科技大学 | Scanner network scanning attack behavior detection method, medium and terminal |
| CN114465764B (en) * | 2021-12-24 | 2024-02-20 | 中孚信息股份有限公司 | Port scanning identification method, system and device based on flow data |
| CN114465764A (en) * | 2021-12-24 | 2022-05-10 | 中孚信息股份有限公司 | Port scanning identification method, system and device based on flow data |
| CN114244632A (en) * | 2022-02-24 | 2022-03-25 | 上海观安信息技术股份有限公司 | Method, device, electronic equipment and medium for detecting network attack behavior of ICMP network scanning |
| CN114244632B (en) * | 2022-02-24 | 2022-05-03 | 上海观安信息技术股份有限公司 | Method, device, electronic equipment and medium for detecting network attack behavior of ICMP network scanning |
| CN114826670A (en) * | 2022-03-23 | 2022-07-29 | 国家计算机网络与信息安全管理中心 | Method for analyzing network flow and detecting large-scale malicious code propagation |
| CN114826670B (en) * | 2022-03-23 | 2024-03-29 | 国家计算机网络与信息安全管理中心 | Method for analyzing network traffic and detecting large-scale malicious code propagation |
| CN114760216A (en) * | 2022-04-12 | 2022-07-15 | 国家计算机网络与信息安全管理中心 | Scanning detection event determination method and device and electronic equipment |
| CN114760216B (en) * | 2022-04-12 | 2023-12-05 | 国家计算机网络与信息安全管理中心 | Method and device for determining scanning detection event and electronic equipment |
| CN115150182A (en) * | 2022-07-25 | 2022-10-04 | 国网湖南省电力有限公司 | Information system network attack detection method based on flow analysis |
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